Theories of Deep Learning 借该课程,进入战略要地的局部战斗中,采用红色字体表示值得深究的概念,以及想起的一些需要注意的地方. Lecture 01 Lecture01: Deep Learning Challenge. Is There Theory? (Donoho/Monajemi/Papyan) Video link 纯粹的简介,意义不大. Lecture 02 Video: Stats385 - Theories of Deep Learning - David…

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本博客是针对李宏毅教授在Youtube上上传的课程视频<ML Lecture 0-1: Introduction of Machine Learning>的学习笔记.在Github上也po了这个系列学习笔记(MachineLearningCourseNote),觉得写的不错的小伙伴欢迎来给项目点个赞哦~~ Lecture 0-1: Introduction of Machine Learning Lecture 0-1: Introduction of Machine Learning Wha…

大咖秀,注意提问环节大家的表情,深入窥探大咖的心态,很有意思. 之前有NG做访谈,现在这成了学术圈流行. Video: https://www.youtube.com/watch?v=oCohnBbmpLA Lecture: https://stats385.github.io/assets/lectures/bolcskei-stats385-slides.pdf 调和分析 reading list:https://www.zhihu.com/question/28661999 先提及了核方法…

CS229 Lecture notes 01 机器学习课程主要分为4部分:监督学习:学习理论:无监督学习:增强学习. $x^{(i)}$表示特征,$y^{(i)}$表示目标,$i=1...m$.m是训练数据的数量.n表示特征的数量. 回归问题:预测连续变量的值. 线性回归: 确定假设$h_{\theta}(x)=\theta_{0}+\theta_{1}x_{1}+\theta_{2}x_{2}$.我们可以增加一个变量$x_{0}=1$,则该假设可以改写为$h(x)=\sum_{i=0}^{x}…

本篇围绕“深度渲染混合模型”展开. Lecture slices Lecture video Reading list A Probabilistic Framework for Deep Learning Semi-Supervised Learning with the Deep Rendering Mixture Model A Probabilistic Theory of Deep Learning 13:49 / 1:30:37 GAN的统计意义:统计假设检验 GAN 一定意义上成为…

Lecturer 咖中咖 Tomaso A. Poggio Lecture slice Lecture video 三个基本问题: Approximation Theory: When and why are deep networks better than shallow networks? Optimization: What is the landscape of the empirical risk? Learning Theory: How can deep learning not…

Web How to do distributed locking Writing Next Generation Reusable JavaScript Modules in ECMAScript 6 Essential Tools for Building SPAs with AngularJS Setting up a TypeScript + Visual Studio Code development environment The Basics of Web Application…

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Database Managing disk space in MongoDB When to use GridFS on MongoDB .NET The Past, Present, and Future of C# Use Mono.Cecil to Reflect Assembly Metadata with Better Performance Using .NET HttpClient to capture partial Responses CLR is Not Allowed M…